1. Field of the Invention
The present invention refers to the monitoring of processes.
2. Description of the Related Art
The growing automatization of processes or procedures of industry or daily life requires checking and control. Thereby, information has to be determined from sensor data, which provide statements about the state of the process and whether the process has already been completed. These statements are determined on the basis of measuring values determined by the sensors based on the process knowledge either by operating staff or by respective algorithms.
In the prior art, a plurality of sensors are usually used, which serve for the evaluation of the process progress, for checking and control of discontinually running processes in liquids, a cleaning step in an industrial plant for food processing for cleaning coatings, etc. If the process progress cannot be measured directly, the sensor data in the control are evaluated whether the process is to be completed or whether a further process step is required.
For the process measurement technology, different sensors are used for measuring respective parameters. These sensors can be conductivity sensors, which, for example, measure conductivity ohmically, via voltammetry or potentiometry. Further, sensors for measuring the optical properties, are used, such as via transmitted light, scattered light, surface reflection measurement or spectroscopy. Further sensors used in the prior art comprise sensors for the acquisition of the viscosity, the permitivity, for example by the acquisition of the impedance or impedance spectroscopy, a temperature, a heat conductivity, a heat capacity or chemical sensors of all types. Thereby, it is normally insignificant whether the sensors are constructed in a conventional way or in a microsystemtechnical way. Further, an integration of the sensors is not critical, as long as the integration position is suitable for the measurement of relative properties.
Apart from the quality of the sensors and the acquisition of the parameters by the sensors, the evaluation of the acquired parameters is of significant importance. Therefore, knowledge-based algorithms, such as a fuzzy logic, are used in the prior art.
However, these methods have a number of disadvantages. On the one hand, it is typically difficult and expensive to determine the connection between the sensor parameters and the state of the process. Disturbance variables, which have not occurred in determining the dependency of the sensor parameters on the state of the process, are difficult to compensate. Generally, knowledge-based algorithms have the disadvantage that they cannot react to unexpected changes. Further, the time changes of the sensor, i.e., a sensor drift, have to be compensated expensively, or a recalibration of the sensor has to be performed.